May 2019 Issue Vol.9 No.5
Bio fertilizers and their role in sustainable Aquaculture with particular reference to Azolla & Spirulina
https://ia601401.us.archive.org/30/items/ijicemay2019/no09vol0501.pdf
N.R.Chattopadhyay1 & P.P.Ghorai2
1Visiting Research Professor, Dept. of Biotechnology, Govt. of India
2 Department of Zoology, Vidyasagar University, West Bengal, India
Abstract:
Bio fertilizers are natural fertilizers that are microbial inoculants of bacteria, algae and fungi (separately or in combination), which may help biological nitrogen fixation for the benefit of plants. They help build up the soil micro-flora and there by the soil health. Bio fertilizer also includes organic fertilizers (manure, etc.) Use of bio-fertilizer is recommended for improving the soil fertility in organic farming. Bio fertilizers are the substance that contains microorganism's living or latent cells. A bio fertilizer increases the nutrients of host plants when applied to their seeds, plant surface or soil by colonizing the rhizosphere of the plant. Bio fertilizers are more cost-effective as compared to chemical fertilizers.
Key words: Bio fertilizers, Azolla , 'symbiotic relationship, Organic fertilizers
AN PERFORMANCE COMPARISON ON SPACE COMPLEXITY OF WEB USER TRACKING FOR CLUSTERING AND CLASSIFIERS
https://ia601401.us.archive.org/30/items/ijicemay2019/no09vol0502.pdf
Mr. N . Ulaganathan
Ph.D. (Part-Time) Research Scholar Department of Computer Science
Nandha Arts and Science College Erode, Tamil Nadu, India
Dr. S. Prasath
Research Supervisor & Ass.Professor, Department of Computer Science
Nandha Arts and Science College Erode, Tamil Nadu, India
Abstract:
Abstract- Web usage mining is the process of examining the web access logs navigation patterns which comprise browsing behaviors of all users over web. The activities of each user on web are stored in the form of weblog files or weblog database. The access activity on web signifies that the number of web pages and the number of times are sequentially visited by the user at different sessions. Through the examination of behavioral navigation patterns, the traffic patterns are mined from weblog database and the future access of the web user is predicted as well as the location of web user is tracked in a significant manner. During the web user behavior analysis, the mining of web traffic pattern is a challenging task. Also, the lack of web traffic pattern mining leads to reduce the performance in the identification of web user location. For the extraction of traffic patterns, the machine learning of clustering and classification techniques are utilized in the mining process to provide accurate results. With this intention, the proposed research work is implemented with web user by effectively mining the patterns from weblog database. The clustering was developed with the aim of predicting the frequent web pages on weblog database browsed by a user but the prediction time remained unaddressed. In order to address the existing issues, three proposed techniques Clustering and Classifier technique based Web Pattern Clustering technique are implemented. The goal of attaining effective web data usage analysis by achieving higher clustering efficiency with less latency. At the beginning process of proposed method to collect the information of all users from weblog database by using server log files. Further, clustering approach is employed to perform similar user interest web pages from the obtained relevant the space complexity.
Keywords: Web Data, Clustering, Classifier, Space complexity.
Read complete May 2019
Read complete May 2019
Mr. N . Ulaganathan
Ph.D. (Part-Time) Research Scholar Department of Computer Science
Nandha Arts and Science College Erode, Tamil Nadu, India
Dr. S. Prasath
Research Supervisor & Ass.Professor, Department of Computer Science
Nandha Arts and Science College Erode, Tamil Nadu, India
Abstract: Abstract- Web usage mining is the process of examining the web access logs navigation patterns which comprise browsing behaviors of all users over web. The activities of each user on web are stored in the form of weblog files or weblog database. The access activity on web signifies that the number of web pages and the number of times are sequentially visited by the user at different sessions. Through the examination of behavioral navigation patterns, the traffic patterns are mined from weblog database and the future access of the web user is predicted as well as the location of web user is tracked in a significant manner. During the web user behavior analysis, the mining of web traffic pattern is a challenging task. Also, the lack of web traffic pattern mining leads to reduce the performance in the identification of web user location. For the extraction of traffic patterns, the machine learning of clustering and classification techniques are utilized in the mining process to provide accurate results. With this intention, the proposed research work is implemented with web user by effectively mining the patterns from weblog database. The clustering was developed with the aim of predicting the frequent web pages on weblog database browsed by a user but the prediction time remained unaddressed. In order to address the existing issues, three proposed techniques Clustering and Classifier technique based Web Pattern Clustering technique are implemented. The goal of attaining effective web data usage analysis by achieving higher clustering efficiency with less latency. At the beginning process of proposed method to collect the information of all users from weblog database by using server log files. Further, clustering approach is employed to perform similar user interest web pages from the obtained relevant the space complexity.
Keywords: Web Data, Clustering, Classifier, Space complexity.